Precision Gait Analysis and Event Detection Using IMUs: A Comparative Evaluation with Insole Pressure Readings

Area of Science:

• Neurology
• Rehabilitation Medicine
• Biomedical Engineering
• Artificial Intelligence in Healthcare

Background:

• Neurological conditions such as Parkinson's disease, multiple sclerosis (MS), and stroke are leading causes of global disability and balance disorders.
• Current telerehabilitation approaches for balance disorders often lack personalization and precise outcome prediction.
• The integration of wearable and IoT devices offers potential for enhanced remote patient monitoring and intervention.

Purpose of the Study:

• To introduce and evaluate the TeleRehaB Decision Support System (DSS), a novel clinical tool for telerehabilitation of balance disorders.
• To leverage artificial intelligence (AI) and connected devices for improved analysis of prognostic factors, therapeutic efficacy, and patient outcomes.
• To develop a system capable of customizing rehabilitation plans based on individual patient characteristics and disorder specifics.

Main Methods:

• Development of the TeleRehaB Decision Support System (DSS) integrating AI with wearable and IoT devices.
• Utilizing the DSS to evaluate prognostic factors, therapeutic efficacy, patient outcomes, and potential adverse effects.
• Customizing patient rehabilitation plans based on disorder type, intervention intensity, technology use, and patient compliance.

Main Results:

• The DSS demonstrated strong positive correlations in predictive capabilities, particularly in two-way tasks.
• The system effectively evaluates key parameters for personalized rehabilitation planning.
• Initial findings suggest advanced predictive accuracy for patient outcomes in balance disorder management.

Conclusions:

• The TeleRehaB DSS represents a significant advancement in managing balance disorders, especially those stemming from neurological deficits.
• The system enhances the precision of balance and gait analysis through AI and wearable technology.
• Personalized rehabilitation plans generated by the DSS have the potential to improve patient outcomes and cost-effectiveness in telerehabilitation.